Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations29531
Missing cells88488
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 MiB
Average record size in memory296.6 B

Variable types

Categorical2
DateTime1
Numeric13

Alerts

AQI is highly overall correlated with AQI_Bucket and 3 other fieldsHigh correlation
AQI_Bucket is highly overall correlated with AQI and 1 other fieldsHigh correlation
Benzene is highly overall correlated with Toluene and 1 other fieldsHigh correlation
CO is highly overall correlated with AQIHigh correlation
NO is highly overall correlated with NOx and 1 other fieldsHigh correlation
NO2 is highly overall correlated with NOx and 1 other fieldsHigh correlation
NOx is highly overall correlated with NO and 2 other fieldsHigh correlation
PM10 is highly overall correlated with AQI and 5 other fieldsHigh correlation
PM2.5 is highly overall correlated with AQI and 1 other fieldsHigh correlation
Toluene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
Xylene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
PM2.5 has 4598 (15.6%) missing values Missing
PM10 has 11140 (37.7%) missing values Missing
NO has 3582 (12.1%) missing values Missing
NO2 has 3585 (12.1%) missing values Missing
NOx has 4185 (14.2%) missing values Missing
NH3 has 10328 (35.0%) missing values Missing
CO has 2059 (7.0%) missing values Missing
SO2 has 3854 (13.1%) missing values Missing
O3 has 4022 (13.6%) missing values Missing
Benzene has 5623 (19.0%) missing values Missing
Toluene has 8041 (27.2%) missing values Missing
Xylene has 18109 (61.3%) missing values Missing
AQI has 4681 (15.9%) missing values Missing
AQI_Bucket has 4681 (15.9%) missing values Missing
Benzene is highly skewed (γ1 = 21.30421849) Skewed
NOx has 740 (2.5%) zeros Zeros
CO has 2328 (7.9%) zeros Zeros
Benzene has 3802 (12.9%) zeros Zeros
Toluene has 2861 (9.7%) zeros Zeros
Xylene has 1747 (5.9%) zeros Zeros

Reproduction

Analysis started2024-11-14 23:55:23.263852
Analysis finished2024-11-14 23:56:20.345416
Duration57.08 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

City
Categorical

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Ahmedabad
2009 
Delhi
2009 
Mumbai
2009 
Bengaluru
2009 
Lucknow
2009 
Other values (21)
19486 

Length

Max length18
Median length12
Mean length8.2757441
Min length5

Characters and Unicode

Total characters244391
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAhmedabad
2nd rowAhmedabad
3rd rowAhmedabad
4th rowAhmedabad
5th rowAhmedabad

Common Values

ValueCountFrequency (%)
Ahmedabad 2009
 
6.8%
Delhi 2009
 
6.8%
Mumbai 2009
 
6.8%
Bengaluru 2009
 
6.8%
Lucknow 2009
 
6.8%
Chennai 2009
 
6.8%
Hyderabad 2006
 
6.8%
Patna 1858
 
6.3%
Gurugram 1679
 
5.7%
Visakhapatnam 1462
 
5.0%
Other values (16) 10472
35.5%

Length

2024-11-14T17:56:20.607076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ahmedabad 2009
 
6.8%
delhi 2009
 
6.8%
mumbai 2009
 
6.8%
bengaluru 2009
 
6.8%
lucknow 2009
 
6.8%
chennai 2009
 
6.8%
hyderabad 2006
 
6.8%
patna 1858
 
6.3%
gurugram 1679
 
5.7%
visakhapatnam 1462
 
5.0%
Other values (16) 10472
35.5%

Most occurring characters

ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Date
Date

Distinct2009
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size230.8 KiB
Minimum2015-01-01 00:00:00
Maximum2020-07-01 00:00:00
2024-11-14T17:56:21.015255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:21.494966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PM2.5
Real number (ℝ)

High correlation  Missing 

Distinct11716
Distinct (%)47.0%
Missing4598
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean67.450578
Minimum0.04
Maximum949.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:21.922101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile13.206
Q128.82
median48.57
Q380.59
95-th percentile193.96
Maximum949.99
Range949.95
Interquartile range (IQR)51.77

Descriptive statistics

Standard deviation64.661449
Coefficient of variation (CV)0.9586493
Kurtosis21.132222
Mean67.450578
Median Absolute Deviation (MAD)23.43
Skewness3.3699599
Sum1681745.3
Variance4181.103
MonotonicityNot monotonic
2024-11-14T17:56:22.360301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 19
 
0.1%
20.75 12
 
< 0.1%
27.82 11
 
< 0.1%
18.81 10
 
< 0.1%
47.43 10
 
< 0.1%
29.75 10
 
< 0.1%
28.45 10
 
< 0.1%
11.81 10
 
< 0.1%
15 10
 
< 0.1%
38.07 9
 
< 0.1%
Other values (11706) 24822
84.1%
(Missing) 4598
 
15.6%
ValueCountFrequency (%)
0.04 1
< 0.1%
0.16 1
< 0.1%
0.24 1
< 0.1%
0.28 1
< 0.1%
0.98 1
< 0.1%
0.99 1
< 0.1%
1.14 1
< 0.1%
1.19 1
< 0.1%
1.25 1
< 0.1%
1.39 1
< 0.1%
ValueCountFrequency (%)
949.99 1
< 0.1%
917.77 1
< 0.1%
916.67 1
< 0.1%
914.94 1
< 0.1%
914.64 1
< 0.1%
894.75 1
< 0.1%
868.66 1
< 0.1%
858.73 1
< 0.1%
832.8 1
< 0.1%
821.42 1
< 0.1%

PM10
Real number (ℝ)

High correlation  Missing 

Distinct12571
Distinct (%)68.4%
Missing11140
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean118.1271
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:22.770515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile26.365
Q156.255
median95.68
Q3149.745
95-th percentile303.34
Maximum1000
Range999.99
Interquartile range (IQR)93.49

Descriptive statistics

Standard deviation90.60511
Coefficient of variation (CV)0.76701373
Kurtosis6.7478735
Mean118.1271
Median Absolute Deviation (MAD)43.92
Skewness2.0531891
Sum2172475.6
Variance8209.2859
MonotonicityNot monotonic
2024-11-14T17:56:23.215263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 9
 
< 0.1%
33.81 7
 
< 0.1%
72.04 6
 
< 0.1%
84.08 6
 
< 0.1%
109.67 6
 
< 0.1%
43.1 6
 
< 0.1%
102.17 6
 
< 0.1%
20.53 6
 
< 0.1%
87.02 6
 
< 0.1%
39.46 6
 
< 0.1%
Other values (12561) 18327
62.1%
(Missing) 11140
37.7%
ValueCountFrequency (%)
0.01 1
< 0.1%
0.02 1
< 0.1%
0.03 1
< 0.1%
0.04 2
< 0.1%
0.06 1
< 0.1%
0.07 1
< 0.1%
0.13 2
< 0.1%
0.14 2
< 0.1%
0.16 1
< 0.1%
0.17 2
< 0.1%
ValueCountFrequency (%)
1000 1
< 0.1%
985 2
< 0.1%
917.08 1
< 0.1%
847.41 1
< 0.1%
802.87 1
< 0.1%
796.88 1
< 0.1%
768.16 1
< 0.1%
763.58 1
< 0.1%
761.91 1
< 0.1%
743.98 1
< 0.1%

NO
Real number (ℝ)

High correlation  Missing 

Distinct5776
Distinct (%)22.3%
Missing3582
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean17.57473
Minimum0.02
Maximum390.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:23.625705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile1.7
Q15.63
median9.89
Q319.95
95-th percentile61.19
Maximum390.68
Range390.66
Interquartile range (IQR)14.32

Descriptive statistics

Standard deviation22.785846
Coefficient of variation (CV)1.2965119
Kurtosis25.164347
Mean17.57473
Median Absolute Deviation (MAD)5.64
Skewness3.8831663
Sum456046.66
Variance519.19479
MonotonicityNot monotonic
2024-11-14T17:56:24.079977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.93 34
 
0.1%
8.78 29
 
0.1%
7.78 29
 
0.1%
0.92 28
 
0.1%
0.97 27
 
0.1%
1.94 27
 
0.1%
0.9 26
 
0.1%
2.89 26
 
0.1%
7.97 26
 
0.1%
0.88 25
 
0.1%
Other values (5766) 25672
86.9%
(Missing) 3582
 
12.1%
ValueCountFrequency (%)
0.02 7
< 0.1%
0.03 3
< 0.1%
0.06 2
 
< 0.1%
0.09 2
 
< 0.1%
0.1 1
 
< 0.1%
0.11 2
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 1
 
< 0.1%
ValueCountFrequency (%)
390.68 1
< 0.1%
382.44 1
< 0.1%
351.3 1
< 0.1%
304.26 1
< 0.1%
289.75 1
< 0.1%
288.55 1
< 0.1%
287.14 1
< 0.1%
273.39 1
< 0.1%
270.09 1
< 0.1%
268.03 1
< 0.1%

NO2
Real number (ℝ)

High correlation  Missing 

Distinct7404
Distinct (%)28.5%
Missing3585
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean28.560659
Minimum0.01
Maximum362.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:24.532451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.93
Q111.75
median21.69
Q337.62
95-th percentile74.125
Maximum362.21
Range362.2
Interquartile range (IQR)25.87

Descriptive statistics

Standard deviation24.474746
Coefficient of variation (CV)0.85693911
Kurtosis11.211125
Mean28.560659
Median Absolute Deviation (MAD)11.42
Skewness2.4645596
Sum741034.86
Variance599.01318
MonotonicityNot monotonic
2024-11-14T17:56:24.998129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.58 24
 
0.1%
9.42 23
 
0.1%
9.14 18
 
0.1%
7.14 17
 
0.1%
9.47 17
 
0.1%
10.09 17
 
0.1%
10.21 17
 
0.1%
9.24 17
 
0.1%
9.44 17
 
0.1%
10.99 16
 
0.1%
Other values (7394) 25763
87.2%
(Missing) 3585
 
12.1%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.02 5
< 0.1%
0.03 9
< 0.1%
0.04 2
 
< 0.1%
0.05 3
 
< 0.1%
0.06 3
 
< 0.1%
0.07 7
< 0.1%
0.08 5
< 0.1%
0.09 7
< 0.1%
0.1 4
< 0.1%
ValueCountFrequency (%)
362.21 1
< 0.1%
292.02 1
< 0.1%
277.31 1
< 0.1%
273.39 1
< 0.1%
266.46 1
< 0.1%
245.62 1
< 0.1%
241.34 1
< 0.1%
239.18 1
< 0.1%
239.1 1
< 0.1%
237.27 1
< 0.1%

NOx
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8156
Distinct (%)32.2%
Missing4185
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean32.309123
Minimum0
Maximum467.63
Zeros740
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:25.443325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q112.82
median23.52
Q340.1275
95-th percentile96.3575
Maximum467.63
Range467.63
Interquartile range (IQR)27.3075

Descriptive statistics

Standard deviation31.646011
Coefficient of variation (CV)0.979476
Kurtosis10.836335
Mean32.309123
Median Absolute Deviation (MAD)12.69
Skewness2.5699146
Sum818907.04
Variance1001.47
MonotonicityNot monotonic
2024-11-14T17:56:25.977992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 740
 
2.5%
4.22 208
 
0.7%
6.24 115
 
0.4%
4.3 35
 
0.1%
2.21 31
 
0.1%
4.95 19
 
0.1%
4.14 18
 
0.1%
4.47 17
 
0.1%
4.97 16
 
0.1%
4.05 14
 
< 0.1%
Other values (8146) 24133
81.7%
(Missing) 4185
 
14.2%
ValueCountFrequency (%)
0 740
2.5%
0.03 4
 
< 0.1%
0.04 9
 
< 0.1%
0.05 3
 
< 0.1%
0.06 2
 
< 0.1%
0.07 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 2
 
< 0.1%
0.12 1
 
< 0.1%
ValueCountFrequency (%)
467.63 1
< 0.1%
382.84 1
< 0.1%
378.31 1
< 0.1%
378.24 1
< 0.1%
302.78 1
< 0.1%
293.1 1
< 0.1%
289.09 1
< 0.1%
287.89 1
< 0.1%
273.33 1
< 0.1%
271.94 1
< 0.1%

NH3
Real number (ℝ)

Missing 

Distinct5922
Distinct (%)30.8%
Missing10328
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean23.483476
Minimum0.01
Maximum352.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:26.366072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.74
Q18.58
median15.85
Q330.02
95-th percentile63.427
Maximum352.89
Range352.88
Interquartile range (IQR)21.44

Descriptive statistics

Standard deviation25.684275
Coefficient of variation (CV)1.0937169
Kurtosis27.964608
Mean23.483476
Median Absolute Deviation (MAD)9.25
Skewness4.0839934
Sum450953.19
Variance659.68198
MonotonicityNot monotonic
2024-11-14T17:56:26.823799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.29 36
 
0.1%
6.32 29
 
0.1%
6.3 28
 
0.1%
6.31 28
 
0.1%
6.28 27
 
0.1%
6.27 24
 
0.1%
10.46 23
 
0.1%
6.59 22
 
0.1%
6.6 21
 
0.1%
6.62 21
 
0.1%
Other values (5912) 18944
64.1%
(Missing) 10328
35.0%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.02 6
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.08 2
 
< 0.1%
0.1 1
 
< 0.1%
0.11 4
< 0.1%
0.12 3
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
352.89 1
< 0.1%
328.89 1
< 0.1%
323.48 1
< 0.1%
309.04 1
< 0.1%
303.53 1
< 0.1%
302.08 1
< 0.1%
301.28 1
< 0.1%
301.18 1
< 0.1%
297.64 1
< 0.1%
296.43 1
< 0.1%

CO
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1779
Distinct (%)6.5%
Missing2059
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean2.2485982
Minimum0
Maximum175.81
Zeros2328
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:27.300282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.51
median0.89
Q31.45
95-th percentile8.0245
Maximum175.81
Range175.81
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation6.9628843
Coefficient of variation (CV)3.0965444
Kurtosis109.48805
Mean2.2485982
Median Absolute Deviation (MAD)0.44
Skewness8.8783215
Sum61773.49
Variance48.481757
MonotonicityNot monotonic
2024-11-14T17:56:27.694425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2328
 
7.9%
0.68 209
 
0.7%
0.85 208
 
0.7%
0.8 205
 
0.7%
0.89 203
 
0.7%
0.78 200
 
0.7%
0.84 200
 
0.7%
0.81 199
 
0.7%
0.64 198
 
0.7%
0.67 194
 
0.7%
Other values (1769) 23328
79.0%
(Missing) 2059
 
7.0%
ValueCountFrequency (%)
0 2328
7.9%
0.01 59
 
0.2%
0.02 59
 
0.2%
0.03 56
 
0.2%
0.04 30
 
0.1%
0.05 48
 
0.2%
0.06 42
 
0.1%
0.07 40
 
0.1%
0.08 34
 
0.1%
0.09 38
 
0.1%
ValueCountFrequency (%)
175.81 1
< 0.1%
145.32 1
< 0.1%
134.85 1
< 0.1%
132.47 1
< 0.1%
132.07 1
< 0.1%
124.01 1
< 0.1%
119.68 1
< 0.1%
119.3 1
< 0.1%
118.02 1
< 0.1%
118 1
< 0.1%

SO2
Real number (ℝ)

Missing 

Distinct4761
Distinct (%)18.5%
Missing3854
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean14.531977
Minimum0.01
Maximum193.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:28.203497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.63
Q15.67
median9.16
Q315.22
95-th percentile46.208
Maximum193.86
Range193.85
Interquartile range (IQR)9.55

Descriptive statistics

Standard deviation18.133775
Coefficient of variation (CV)1.2478532
Kurtosis22.067101
Mean14.531977
Median Absolute Deviation (MAD)4.12
Skewness4.0836596
Sum373137.58
Variance328.83379
MonotonicityNot monotonic
2024-11-14T17:56:28.687841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.74 36
 
0.1%
6.12 35
 
0.1%
6.61 32
 
0.1%
4.65 32
 
0.1%
5.81 32
 
0.1%
5.53 32
 
0.1%
5.57 31
 
0.1%
6.47 31
 
0.1%
5.95 31
 
0.1%
5.13 30
 
0.1%
Other values (4751) 25355
85.9%
(Missing) 3854
 
13.1%
ValueCountFrequency (%)
0.01 1
< 0.1%
0.04 1
< 0.1%
0.21 1
< 0.1%
0.26 1
< 0.1%
0.36 1
< 0.1%
0.41 2
< 0.1%
0.42 1
< 0.1%
0.44 1
< 0.1%
0.48 1
< 0.1%
0.49 1
< 0.1%
ValueCountFrequency (%)
193.86 1
< 0.1%
187.02 1
< 0.1%
186.08 1
< 0.1%
182.39 1
< 0.1%
180.85 1
< 0.1%
179.18 1
< 0.1%
178.93 1
< 0.1%
178.63 1
< 0.1%
178.58 1
< 0.1%
176.88 1
< 0.1%

O3
Real number (ℝ)

Missing 

Distinct7699
Distinct (%)30.2%
Missing4022
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean34.49143
Minimum0.01
Maximum257.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:29.236582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile7.02
Q118.86
median30.84
Q345.57
95-th percentile74.142
Maximum257.73
Range257.72
Interquartile range (IQR)26.71

Descriptive statistics

Standard deviation21.694928
Coefficient of variation (CV)0.62899474
Kurtosis3.4294645
Mean34.49143
Median Absolute Deviation (MAD)12.96
Skewness1.3301193
Sum879841.9
Variance470.66991
MonotonicityNot monotonic
2024-11-14T17:56:29.743337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.48 17
 
0.1%
23.6 15
 
0.1%
22.14 15
 
0.1%
19.64 14
 
< 0.1%
18.33 14
 
< 0.1%
22.94 13
 
< 0.1%
19.68 13
 
< 0.1%
13.14 13
 
< 0.1%
32.06 13
 
< 0.1%
31.95 12
 
< 0.1%
Other values (7689) 25370
85.9%
(Missing) 4022
 
13.6%
ValueCountFrequency (%)
0.01 4
< 0.1%
0.02 7
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 3
 
< 0.1%
0.07 1
 
< 0.1%
0.1 8
< 0.1%
0.11 2
 
< 0.1%
0.12 1
 
< 0.1%
ValueCountFrequency (%)
257.73 1
< 0.1%
200.41 1
< 0.1%
193.31 1
< 0.1%
186.07 1
< 0.1%
177.07 1
< 0.1%
175.04 1
< 0.1%
172.28 1
< 0.1%
169.36 1
< 0.1%
169.35 1
< 0.1%
165.48 1
< 0.1%

Benzene
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct1873
Distinct (%)7.8%
Missing5623
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean3.2808403
Minimum0
Maximum455.03
Zeros3802
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:30.128509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.12
median1.07
Q33.08
95-th percentile9.72
Maximum455.03
Range455.03
Interquartile range (IQR)2.96

Descriptive statistics

Standard deviation15.811136
Coefficient of variation (CV)4.8192338
Kurtosis530.17147
Mean3.2808403
Median Absolute Deviation (MAD)1.06
Skewness21.304218
Sum78438.33
Variance249.99203
MonotonicityNot monotonic
2024-11-14T17:56:30.566733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3802
 
12.9%
0.03 300
 
1.0%
0.02 292
 
1.0%
0.01 217
 
0.7%
0.04 190
 
0.6%
0.05 176
 
0.6%
0.09 170
 
0.6%
2 170
 
0.6%
0.1 167
 
0.6%
0.08 157
 
0.5%
Other values (1863) 18267
61.9%
(Missing) 5623
 
19.0%
ValueCountFrequency (%)
0 3802
12.9%
0.01 217
 
0.7%
0.02 292
 
1.0%
0.03 300
 
1.0%
0.04 190
 
0.6%
0.05 176
 
0.6%
0.06 146
 
0.5%
0.07 123
 
0.4%
0.08 157
 
0.5%
0.09 170
 
0.6%
ValueCountFrequency (%)
455.03 1
< 0.1%
454.85 1
< 0.1%
449.38 1
< 0.1%
448.59 1
< 0.1%
445.83 1
< 0.1%
443.63 1
< 0.1%
438.01 1
< 0.1%
435.9 1
< 0.1%
435.09 1
< 0.1%
432.94 1
< 0.1%

Toluene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct3608
Distinct (%)16.8%
Missing8041
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean8.7009721
Minimum0
Maximum454.85
Zeros2861
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:31.032454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median2.97
Q39.15
95-th percentile33.92
Maximum454.85
Range454.85
Interquartile range (IQR)8.55

Descriptive statistics

Standard deviation19.969164
Coefficient of variation (CV)2.2950497
Kurtosis216.74551
Mean8.7009721
Median Absolute Deviation (MAD)2.94
Skewness11.666129
Sum186983.89
Variance398.7675
MonotonicityNot monotonic
2024-11-14T17:56:31.459656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2861
 
9.7%
0.02 111
 
0.4%
0.03 102
 
0.3%
0.05 99
 
0.3%
0.04 86
 
0.3%
1.1 83
 
0.3%
6 79
 
0.3%
0.08 76
 
0.3%
0.06 72
 
0.2%
0.01 70
 
0.2%
Other values (3598) 17851
60.4%
(Missing) 8041
27.2%
ValueCountFrequency (%)
0 2861
9.7%
0.01 70
 
0.2%
0.02 111
 
0.4%
0.03 102
 
0.3%
0.04 86
 
0.3%
0.05 99
 
0.3%
0.06 72
 
0.2%
0.07 61
 
0.2%
0.08 76
 
0.3%
0.09 54
 
0.2%
ValueCountFrequency (%)
454.85 1
< 0.1%
454.12 1
< 0.1%
449.14 1
< 0.1%
448.87 1
< 0.1%
445.84 1
< 0.1%
443.63 1
< 0.1%
437.77 1
< 0.1%
435.94 1
< 0.1%
434.92 1
< 0.1%
433.02 1
< 0.1%

Xylene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1561
Distinct (%)13.7%
Missing18109
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean3.0701278
Minimum0
Maximum170.37
Zeros1747
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:31.788794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.14
median0.98
Q33.35
95-th percentile12.558
Maximum170.37
Range170.37
Interquartile range (IQR)3.21

Descriptive statistics

Standard deviation6.3232474
Coefficient of variation (CV)2.059604
Kurtosis119.98012
Mean3.0701278
Median Absolute Deviation (MAD)0.98
Skewness7.8915153
Sum35067
Variance39.983458
MonotonicityNot monotonic
2024-11-14T17:56:32.180583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1747
 
5.9%
0.1 255
 
0.9%
2 142
 
0.5%
0.65 120
 
0.4%
0.12 108
 
0.4%
0.11 93
 
0.3%
0.13 80
 
0.3%
0.15 80
 
0.3%
0.16 77
 
0.3%
0.52 76
 
0.3%
Other values (1551) 8644
29.3%
(Missing) 18109
61.3%
ValueCountFrequency (%)
0 1747
5.9%
0.01 68
 
0.2%
0.02 50
 
0.2%
0.03 52
 
0.2%
0.04 42
 
0.1%
0.05 52
 
0.2%
0.06 56
 
0.2%
0.07 72
 
0.2%
0.08 62
 
0.2%
0.09 62
 
0.2%
ValueCountFrequency (%)
170.37 1
< 0.1%
137.45 1
< 0.1%
125.18 1
< 0.1%
116.62 1
< 0.1%
109.23 1
< 0.1%
105.76 1
< 0.1%
94.48 1
< 0.1%
89.7 1
< 0.1%
84.72 1
< 0.1%
81.26 1
< 0.1%

AQI
Real number (ℝ)

High correlation  Missing 

Distinct829
Distinct (%)3.3%
Missing4681
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean166.46358
Minimum13
Maximum2049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2024-11-14T17:56:32.823497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile50
Q181
median118
Q3208
95-th percentile407
Maximum2049
Range2036
Interquartile range (IQR)127

Descriptive statistics

Standard deviation140.69659
Coefficient of variation (CV)0.84520941
Kurtosis21.423727
Mean166.46358
Median Absolute Deviation (MAD)48
Skewness3.3967572
Sum4136620
Variance19795.529
MonotonicityNot monotonic
2024-11-14T17:56:33.293866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 223
 
0.8%
100 222
 
0.8%
106 208
 
0.7%
70 208
 
0.7%
78 198
 
0.7%
98 195
 
0.7%
104 192
 
0.7%
66 192
 
0.7%
80 190
 
0.6%
92 187
 
0.6%
Other values (819) 22835
77.3%
(Missing) 4681
 
15.9%
ValueCountFrequency (%)
13 1
 
< 0.1%
14 3
 
< 0.1%
15 3
 
< 0.1%
16 4
 
< 0.1%
17 7
 
< 0.1%
18 2
 
< 0.1%
19 27
0.1%
20 29
0.1%
21 7
 
< 0.1%
22 8
 
< 0.1%
ValueCountFrequency (%)
2049 1
< 0.1%
1917 1
< 0.1%
1842 1
< 0.1%
1747 1
< 0.1%
1719 1
< 0.1%
1672 1
< 0.1%
1646 1
< 0.1%
1630 1
< 0.1%
1613 1
< 0.1%
1595 1
< 0.1%

AQI_Bucket
Categorical

High correlation  Missing 

Distinct6
Distinct (%)< 0.1%
Missing4681
Missing (%)15.9%
Memory size1.7 MiB
Moderate
8829 
Satisfactory
8224 
Poor
2781 
Very Poor
2337 
Good
1341 

Length

Max length12
Median length9
Mean length8.6466398
Min length4

Characters and Unicode

Total characters214869
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowVery Poor
3rd rowSevere
4th rowSevere
5th rowSevere

Common Values

ValueCountFrequency (%)
Moderate 8829
29.9%
Satisfactory 8224
27.8%
Poor 2781
 
9.4%
Very Poor 2337
 
7.9%
Good 1341
 
4.5%
Severe 1338
 
4.5%
(Missing) 4681
15.9%

Length

2024-11-14T17:56:33.663452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-14T17:56:34.015650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
moderate 8829
32.5%
satisfactory 8224
30.2%
poor 5118
18.8%
very 2337
 
8.6%
good 1341
 
4.9%
severe 1338
 
4.9%

Most occurring characters

ValueCountFrequency (%)
o 29971
13.9%
r 25846
12.0%
a 25277
11.8%
t 25277
11.8%
e 24009
11.2%
y 10561
 
4.9%
d 10170
 
4.7%
S 9562
 
4.5%
M 8829
 
4.1%
c 8224
 
3.8%
Other values (8) 37143
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 29971
13.9%
r 25846
12.0%
a 25277
11.8%
t 25277
11.8%
e 24009
11.2%
y 10561
 
4.9%
d 10170
 
4.7%
S 9562
 
4.5%
M 8829
 
4.1%
c 8224
 
3.8%
Other values (8) 37143
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 29971
13.9%
r 25846
12.0%
a 25277
11.8%
t 25277
11.8%
e 24009
11.2%
y 10561
 
4.9%
d 10170
 
4.7%
S 9562
 
4.5%
M 8829
 
4.1%
c 8224
 
3.8%
Other values (8) 37143
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 29971
13.9%
r 25846
12.0%
a 25277
11.8%
t 25277
11.8%
e 24009
11.2%
y 10561
 
4.9%
d 10170
 
4.7%
S 9562
 
4.5%
M 8829
 
4.1%
c 8224
 
3.8%
Other values (8) 37143
17.3%

Interactions

2024-11-14T17:56:14.436008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:25.744856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:29.490685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:33.523261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:37.250400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:41.036692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:44.581277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:48.590674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:52.709165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:56.580683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:02.264912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:06.096205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:09.703323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:14.723491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:26.037467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:29.823301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:33.731340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:37.479728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:41.322174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:44.858924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:48.883952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:52.981786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:56.905328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:02.573377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:06.373449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:09.996635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:15.015122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:26.298086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:30.103949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:34.039002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:37.672817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:41.541203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:45.110059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:49.189550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:53.304434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:57.384815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:02.961090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:06.648536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:10.280998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:15.358322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:26.598245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:30.523702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:34.373182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:37.935886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:41.770795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:45.403793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:49.522144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:53.604617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:57.864114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:03.341235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:06.940209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:10.833096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:15.588700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:26.880858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:30.897192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:34.672428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:38.259363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:42.077306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:45.598444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:49.771758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:53.842694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:58.401504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:03.581368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:07.246843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:11.214806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:15.825778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:27.183496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:31.255996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:34.984086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:38.624682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:42.343971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:45.836509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:50.028867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:54.215504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:59.199140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:03.851462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:07.473949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:11.605609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:16.088424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:27.450128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:31.523961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:35.284735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:39.119298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:42.647688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:46.143931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:50.371945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:54.542596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:59.640216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:04.176193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:07.684020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:12.009599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:16.399053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:27.707200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:31.776584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:35.512796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:39.418887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:42.918000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:46.468099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:50.736253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:54.930492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:00.091968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:04.472879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:07.928112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:12.434559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:16.707712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:28.000809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:32.128214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:35.769417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:39.639957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:43.248659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:46.787721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:51.126765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:55.272675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:00.524096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:04.838424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:08.249301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:12.772979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:17.023919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:28.277422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:32.394845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:36.089629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:39.846664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:43.512240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:47.062385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:51.437585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:55.499722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:01.019951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:05.126645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:08.513791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:13.227606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:17.333549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:28.539135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:32.661955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:36.373327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:40.118257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:43.704257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:47.340495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:51.707758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:55.703782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:01.399806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:05.389853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:08.830407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:13.578850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:17.585143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:28.794757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:32.954576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:36.635433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:40.403886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:43.949358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:47.639106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:51.944507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:55.936856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:01.625590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:05.567905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:09.155709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:13.815959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:17.839253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:29.180061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:33.273199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:36.912681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:40.705078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:44.268048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:48.018074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:52.329126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:55:56.250501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:01.890222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:05.805515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:09.475452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-14T17:56:14.106588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-11-14T17:56:34.332235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AQIAQI_BucketBenzeneCOCityNH3NONO2NOxO3PM10PM2.5SO2TolueneXylene
AQI1.0000.5780.2550.5490.2390.3660.4700.4620.4850.2810.8710.8460.3790.3670.176
AQI_Bucket0.5781.0000.0250.2690.3760.1670.1830.2420.2210.1470.5200.4260.2050.1250.056
Benzene0.2550.0251.0000.2700.1050.1220.2560.3400.2730.1580.3110.2210.1700.7980.752
CO0.5490.2690.2701.0000.2110.2410.3550.2750.3340.0680.3310.3810.2600.3910.382
City0.2390.3760.1050.2111.0000.2300.1430.1900.1790.1680.2140.1680.2600.1400.073
NH30.3660.1670.1220.2410.2301.0000.3470.4820.3180.2020.3820.3900.0840.115-0.107
NO0.4700.1830.2560.3550.1430.3471.0000.4830.756-0.0600.5100.4270.3410.2500.264
NO20.4620.2420.3400.2750.1900.4820.4831.0000.6500.3110.5100.4600.2470.4170.248
NOx0.4850.2210.2730.3340.1790.3180.7560.6501.0000.0400.5410.4390.3510.3140.244
O30.2810.1470.1580.0680.1680.202-0.0600.3110.0401.0000.2840.2710.1990.2440.104
PM100.8710.5200.3110.3310.2140.3820.5100.5100.5410.2841.0000.8800.4010.4040.185
PM2.50.8460.4260.2210.3810.1680.3900.4270.4600.4390.2710.8801.0000.2780.2830.203
SO20.3790.2050.1700.2600.2600.0840.3410.2470.3510.1990.4010.2781.0000.3210.248
Toluene0.3670.1250.7980.3910.1400.1150.2500.4170.3140.2440.4040.2830.3211.0000.648
Xylene0.1760.0560.7520.3820.073-0.1070.2640.2480.2440.1040.1850.2030.2480.6481.000

Missing values

2024-11-14T17:56:18.302958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-14T17:56:19.010801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-14T17:56:19.834758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CityDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
0Ahmedabad2015-01-01NaNNaN0.9218.2217.15NaN0.9227.64133.360.000.020.00NaNNaN
1Ahmedabad2015-01-02NaNNaN0.9715.6916.46NaN0.9724.5534.063.685.503.77NaNNaN
2Ahmedabad2015-01-03NaNNaN17.4019.3029.70NaN17.4029.0730.706.8016.402.25NaNNaN
3Ahmedabad2015-01-04NaNNaN1.7018.4817.97NaN1.7018.5936.084.4310.141.00NaNNaN
4Ahmedabad2015-01-05NaNNaN22.1021.4237.76NaN22.1039.3339.317.0118.892.78NaNNaN
5Ahmedabad2015-01-06NaNNaN45.4138.4881.50NaN45.4145.7646.515.4210.831.93NaNNaN
6Ahmedabad2015-01-07NaNNaN112.1640.62130.77NaN112.1632.2833.470.000.000.00NaNNaN
7Ahmedabad2015-01-08NaNNaN80.8736.7496.75NaN80.8738.5431.890.000.000.00NaNNaN
8Ahmedabad2015-01-09NaNNaN29.1631.0048.00NaN29.1658.6825.750.000.000.00NaNNaN
9Ahmedabad2015-01-10NaNNaNNaN7.040.00NaNNaN8.294.550.000.000.00NaNNaN
CityDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
29521Visakhapatnam2020-06-2233.17108.225.5842.4527.0613.700.7313.6534.853.9910.242.3295.0Satisfactory
29522Visakhapatnam2020-06-2325.4083.382.7634.0919.9213.130.5410.4043.272.8812.031.33100.0Satisfactory
29523Visakhapatnam2020-06-2434.3690.901.2223.3813.1214.450.5610.9235.122.993.151.6086.0Satisfactory
29524Visakhapatnam2020-06-2513.4558.542.3021.6013.0912.270.418.1929.381.285.640.9277.0Satisfactory
29525Visakhapatnam2020-06-267.6332.275.9123.2717.1911.150.466.8719.901.455.371.4547.0Good
29526Visakhapatnam2020-06-2715.0250.947.6825.0619.5412.470.478.5523.302.2412.070.7341.0Good
29527Visakhapatnam2020-06-2824.3874.093.4226.0616.5311.990.5212.7230.140.742.210.3870.0Satisfactory
29528Visakhapatnam2020-06-2922.9165.733.4529.5318.3310.710.488.4230.960.010.010.0068.0Satisfactory
29529Visakhapatnam2020-06-3016.6449.974.0529.2618.8010.030.529.8428.300.000.000.0054.0Satisfactory
29530Visakhapatnam2020-07-0115.0066.000.4026.8514.055.200.592.1017.05NaNNaNNaN50.0Good